Summary

Row

confirmed

36,691

active

34,682 (94.5%)

death

2,009 (5.5%)

recovered

22,488 (61.3%)

Row

Daily cumulative cases by type (Romania only)

Comparison

Column Column

Daily new confirmed cases

Cases distribution by type

Map

World map of cases (use + and - icons to zoom in/out)

About

The Coronavirus Dashboard: the case of Romania

This Coronavirus dashboard: the case of Romania provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Romania. This dashboard is built with R using the R Markdown framework and was adapted from this dashboard by Rami Krispin.

Code

The code behind this dashboard is available on GitHub.

Data

The input data for this dashboard is the dataset available from the {coronavirus} R package. Make sure to download the development version of the package to have the latest data:

install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.

Information

More information about this dashboard (and how to replicate it for your own country) can be found in this article.

Update

The data is as of Saturday July 18, 2020 and the dashboard has been updated on Monday July 20, 2020.


Go back to www.statsandr.com (blog) or www.antoinesoetewey.com (personal website).

---
title: covid-2020-Romania

output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
    vertical_layout: fill
    theme: bootstrap
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)

#install.packages("devtools")
#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)

library(coronavirus)
data(coronavirus)
#coronavirus_df <- read.csv("https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv", stringsAsFactors = FALSE)
#coronavirus_df$date <- as.Date(coronavirus_df$date)

# coronavirus_df <- coronavirus %>%
#           dplyr::filter(country == "Romania"|country == "Hungary"|country == "Greece"|country == "Sweden")

# View(coronavirus)
# max(coronavirus$date)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  dplyr::filter(country == "Romania") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup()

df_daily <- coronavirus %>%
  dplyr::filter(country == "Romania") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active = confirmed - death ) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```

Summary
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


### active {.value-box} 

```{r} 
valueBox(
value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1), 
"%)",
sep = "" 
), 
caption = "Active cases (% of total cases)", icon = "fas fa-bed", 
 color = active_color 
 ) 
```


### death {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Death cases (death rate)",
  icon = "fas fa-frown",
  color = death_color
)
```

### recovered {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Recovered cases (Recovery rate)",
  icon = "fas fa-user-check",
  color = recovered_color
)
```

Row
-----------------------------------------------------------------------

### **Daily cumulative cases by type** (Romania only)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmed",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-04"),
    y = 1,
    text = paste("First case"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-11"),
    y = 3,
    text = paste("First death"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-18"),
    y = 14,
    text = paste(
      "Lockdown"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative number of cases"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Comparison
=======================================================================


Column Column {.tabset}
-------------------------------------


### **Daily new confirmed cases**
    
```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-29") %>%
  dplyr::mutate(country = country) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Romania,
    type = "scatter",
    mode = "lines+markers",
    name = "Romania"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Hungary,
    type = "scatter",
    mode = "lines+markers",
    name = "Hungary"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Greece,
    type = "scatter",
    mode = "lines+markers",
    name = "Greece"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Sweden,
    type = "scatter",
    mode = "lines+markers",
    name = "Sweden"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "New confirmed cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```
 
### **Cases distribution by type**

```{r daily_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Romania" |
    country == "Greece" |
    country == "Hungary" |
    country == "Sweden") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
  data = df_EU,
  x = ~country,
  # y = ~unrecovered,
  y = ~ confirmed,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Confirmed",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


Map
=======================================================================

### **World map of cases** (*use + and - icons to zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  #dplyr::filter(country == "Romania") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(country, province, lat, long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~long, lat = ~lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "country", "province")
        ),
        group = df,
        #                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })
map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```





About
=======================================================================

**The Coronavirus Dashboard: the case of Romania**

This [Coronavirus dashboard: the case of Romania](https://www.antoinesoetewey.com/files/coronavirus-dashboard.html) provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Romania. This dashboard is built with R using the R Markdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.

**Data**

The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:

```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.

**Information**

More information about this dashboard (and how to replicate it for your own country) can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).

**Update**

The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.


*Go back to [www.statsandr.com](https://www.statsandr.com/) (blog) or [www.antoinesoetewey.com](https://www.antoinesoetewey.com/) (personal website)*.